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Document Clustering Workbench added for easy experimenting with Carrot² clustering, radically simplified Java API, search results clustering web application re-implemented, user manual [5] available 2.1.0 August 2007 Document Clustering Server added for exposing clustering as a REST service 2.0.0 September 2006
In computer programming, primary clustering is a phenomenon that causes performance degradation in linear-probing hash tables.The phenomenon states that, as elements are added to a linear probing hash table, they have a tendency to cluster together into long runs (i.e., long contiguous regions of the hash table that contain no free slots).
Solr (pronounced "solar") is an open-source enterprise-search platform, written in Java.Its major features include full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features [2] and rich document (e.g., Word, PDF) handling.
Stop-and-copy garbage collection in a Lisp architecture: [1] Memory is divided into working and free memory; new objects are allocated in the former. When it is full (depicted), garbage collection is performed: All data structures still in use are located by pointer tracing and copied into consecutive locations in free memory.
OrthoFinder: [5] a fast, scalable and accurate method for clustering proteins into gene families (orthogroups) [6] [7] Linclust: [8] first algorithm whose runtime scales linearly with input set size, very fast, part of MMseqs2 [9] software suite for fast, sensitive sequence searching and clustering of large sequence sets
In statistics and data mining, affinity propagation (AP) is a clustering algorithm based on the concept of "message passing" between data points. [1] Unlike clustering algorithms such as k-means or k-medoids, affinity propagation does not require the number of clusters to be determined or estimated before running the algorithm.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]